Fine-Tuning a Pre-trained Model
Fine-tuning is a transfer learning technique that involves taking a pre-trained model and making small adjustments to it to improve its performance on a specific task. This approach is particularly beneficial in scenarios where we have limited labeled data for the target task, as it allows us to leverage the knowledge captured by the model during its initial training on a large dataset.
Why Fine-Tuning?
Pre-trained models are trained on large datasets and can generalize well to a variety of tasks. However, they might not be perfectly suited for your specific application. Fine-tuning helps tailor these models to better fit your data. Some key reasons to fine-tune include: - Improved Performance: Fine-tuning can lead to better accuracy and performance metrics than training a model from scratch. - Reduced Training Time: Since the model has already learned useful representations, fine-tuning generally requires less time and fewer resources than training a model from scratch. - Less Data Required: Fine-tuning can yield good results even with smaller datasets, which is often a limitation in many practical applications.How to Fine-Tune a Model in Keras
To fine-tune a pre-trained model using Keras, follow these steps:Step 1: Load a Pre-trained Model
You can load a pre-trained model directly from Keras. For instance, let's use VGG16, a popular model for image classification tasks:`python
from keras.applications import VGG16
from keras.models import Model
Load VGG16 without the top classifier layers
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))`Step 2: Add Custom Layers
After loading the base model, you can add your own layers that are specific to your task. This can be done as follows:`python
from keras.layers import Dense, GlobalAveragePooling2D
Add custom layers
x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) x = Dense(num_classes, activation='softmax')(x)num_classes is the number of classes in your dataset
Create the final model
model = Model(inputs=base_model.input, outputs=x)`Step 3: Freeze Layers
To prevent the weights of the pre-trained layers from being updated during the initial fine-tuning phase, freeze those layers:`python
for layer in base_model.layers:
layer.trainable = False
`
Step 4: Compile the Model
Compile the model with an appropriate optimizer and loss function:`python
from keras.optimizers import Adam
model.compile(optimizer=Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
`
Step 5: Train the Model
Now, you can train the model on your dataset:`python
model.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))
`
Step 6: Unfreeze and Fine-Tune
After training the new layers, you can unfreeze some of the layers in the base model to fine-tune the entire model:`python
for layer in base_model.layers[-4:]:
Unfreeze the last 4 layers
layer.trainable = TrueRe-compile the model
model.compile(optimizer=Adam(lr=0.00001), loss='categorical_crossentropy', metrics=['accuracy'])Continue training
model.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))`